Net

case class Net(layers: LazyList[Layer])

The methods of a neural network.

Get the prediction for an input:

net.predict(List(0.4, 0.05, 0.2))

Fit network to a single observation:

net.fit(0.1, List(0.4, 0.05, 0.2), List(0.03. 0.8))

Get the JSON representation of the network:

net.json()

trait Serializable

trait Product

trait Equals

class Object

trait Matchable

class Any

Value members

Returns the neural network with its weights adjusted to the provided observation.

In order for it to be trained, it should fit with multiple observations, usually by folding over an iterator.

Value Params

expectedOutput: The expected output of the observation. It's size should be equal to the size of the output layer.
inputValues: The feature values of the observation.
learningRate: A number that controls how much the weights are adjusted to the observation.

Returns

A new neural network that has the same shape of the original, but it has learned from a single observation.

The JSON representation of the neural network.

Returns the neural network with its weights adjusted to the provided observation.

The calculation is performed in parallel. When the neural network has huge layers, the parallel calculation boosts the performance.

Value Params

expectedOutput: The expected output of the observation. It's size should be equal to the size of the output layer.
inputValues: The feature values of the observation.
learningRate: A number that controls how much the weights are adjusted to the observation.

Returns

A new neural network that has the same shape of the original, but it has learned from a single observation.

Makes a prediction for the provided input.

The calculation is performed in parallel. When the neural network has huge layers, the parallel calculation boosts the performance.

Value Params

inputValues: The values of the features. Their size should be equal to the size of the input layer.

Returns

The prediction. It's size should be equal to the size of the output layer.

Makes a prediction for the provided input.

Value Params

inputValues: The values of the features. Their size should be equal to the size of the input layer.

Returns

The prediction. It's size should be equal to the size of the output layer.

An SVG representation of the neural network.

Returns: The SVG representation of the neural network. The color of each neuron depends on its activation function while the transparency of the synapses depends on their weight.